Improving machine learning iteration speed with faster application build and packaging

What this post added

This post details significant improvements to the ML/AI developer workflow by addressing slow build times and inefficiencies in packaging and distributing execution files. It introduces solutions like leveraging Buck2 with remote execution to mitigate build non-determinism and implementing a Content Addressable Filesystem (CAF) with Btrfs for incremental packaging and distribution of Python executables. These changes have led to double-digit percentage reductions in overhead for ML engineers.